《Paper》

Abstract: We develop an urban growth model where human capital spillovers foster entrepreneurship and learning in heterogeneous cities. Incumbent residents limit city expansion through planning regulations so that commuting and housing costs do not outweigh productivity gains from agglomeration. The model builds on strong microfoundations, matches key regularities at the city and economy-wide levels, and generates novel predictions for which we provide evidence. It can be quantified relying on few parameters, provides a basis to estimate the main ones, and remains transparent regarding its mechanisms. We examine various counterfactuals to assess the effect of cities on economic growth and aggregate output quantitatively.

【論文１（PDF）】【論文２（PDF）】

要旨：New ideas and technologies adopted by a small number of individuals occasionally spread globally through a complex web of social ties. Here, we present a simple and general approximation method, namely, a message-passing approach, that allows us to describe the diffusion processes on (sparse) random networks in an almost exact manner. We consider two classes of binary-action games where the best pure strategies for individual players are characterized as variants of the threshold rule. We verify that the dynamics of diffusion observed on synthetic networks are accurately replicated by the message-passing equation, whose fixed point corresponds to a Nash equilibrium, while the conventional mean-field method tends to overestimate the size and frequency of diffusion. Generalized cascade conditions under which a global diffusion can occur are also provided. We extend the framework to analyze diffusion of multiple goods.

【論文（PDF）】

要旨：携帯電話位置情報データを用いると、国全体などの広範囲の長距離旅行量とその詳細な時間的な変化をかなり精度よく把握できる。このデータを用いて2015年の北陸新幹線・金沢開業前後で前後1年間の比較をすると、所要時間が短縮された場所の変化だけでなく、新幹線で接続されていない西日本各地から石川県への来訪者の増加も大きくみられることがわかる。本研究では、後者のような空間パターンの変化に着目し、その特徴を解明するために、多時点の居住地-旅行先分布表を分解して、近年に発生した類似の変化を探索的に検出することを試みた。具体的には、都道府県あるいは市区町村単位の居住地・旅行先ペアごとの旅行先選択確率の変化を、対称行列（交通サービス変化による直接的な変化を含む対称な変化）と旅行先ごとに均一な値の入る行列（全国から均一に旅行者数を増やす効果）に分解した。その結果として、(1)居住地-旅行先表の経年変化は２種類の空間パターンでほとんど説明できること、(2) 北陸新幹線開業では後者のパターン変化が大きかったこと、(3)後者の効果は3年以上継続しており短期的な広告効果ではないこと、(4)後者の効果がない新幹線開業地も存在すること、などを明らかにした。

要旨：With the rapid increase in the availability of passive data in the field of transportation, combining machine learning with transportation models has emerged as an important research topic in recent years. This study proposes an entropy Tucker model that integrates (1) a Tucker decomposition technique, i.e., an existing machine learning method, and (2) an entropy maximizing model, i.e., an existing model used for modeling trip distribution in the field of transportation. In addition, an optimization algorithm is presented to empirically identify the proposed model. The proposed model provides a solid theoretical foundation for the machine learning method, substantially improves prediction performance, and provides richer behavioral implications through empirical parameter estimation of travel impedance. We empirically apply the proposed method to evaluate the impacts of changes in the public transport fare structure on the destination choice of public transport users in Hiroshima by using the smart card data collected in Hiroshima, Japan. The estimated values of travel time range from 1.146 to 14.44 JPY/min, which is consistent with that reported in existing studies. The results of scenario analysis with different public transport fare structures suggest that identified changes in trip patterns, revenues, and users’ benefits for the public transport operator are considerably different between the conventional entropy model and the proposed entropy Tucker model. Further, we confirm that the users’ benefits vary based on the time of day. These obtained results confirm the importance of considering the heterogeneous preferences of users in economic appraisals.

要旨：土地利用モデルのパラメータは，実用上の問題により、いくつかの段階に分けて推定・キャリブレートされることが多い。しかし、理論的には一括で推定することが望ましく、ゆえに現状の推定手法の信頼性は定かでない。そこで、ベイズ法により全パラメータを一括で推定することを試みた。具体的には、素朴な土地利用モデルに対して、パラメータ推定、変数選択、現況再現性の確認、感度分析を行った。さらに、集積の経済の概念を導入したモデルへの拡張可能性も検討・検証した。推定結果や従来手法との比較から、ベイズ法を用いた推定手法の将来性が示唆された。

《発表論文》

要旨：Older people are less mobile than young people are. Population aging thus means more people would be trapped in locations affected by a shock, preventing the economy from smoothing out spatial differences in labor market outcomes. However, the existence of a large share of immobile workers may mitigate their welfare effects by delaying the capital supply adjustment that would be caused by a flow of workers. In order to study how population aging affects the welfare effects of a local shock, this paper develops a dynamic spatial specific-factor model with demographics that change dynamically depending on fertility rates. Individuals decide where to live and whether to work. Their choices vary over the life cycle because the expected working lifetime and fundamentals (e.g., mobility costs) vary with demographic factors. Hence, aggregate labor adjustment depends on the economy’s age structure. Forward-looking landlords accumulate location-specific capital, and the dynamics of labor and capital interact with each other. I apply the model to Japan and find that population aging can mitigate the welfare loss of workers in a location affected by a negative shock.

要旨：This paper theoretically investigates the relationship between costs for telework and the location of firms and households in a city. I extend the model of Ogawa and Fujita (1980) by introducing two types of companies with different technologies; one is teleworking company hiring more teleworkers and the other is an office company hiring more on-site workers. Only on-site workers conduct face-to-face communication with other firms and incur communication costs.  This paper shows that (i) when telework cost decreases, the first teleworking companies appear in either of two candidate locations: the edge of the existing central business districts (called CBD fringe) or the edge of the residential districts (called urban fringe). If face-to-face communication cost is high, and commuting cost and the ratio of labor input for teleworkers in telework companies are low, then the first telework company is located at the CBD fringe;  otherwise at the urban fringe. (ii) When the telework cost further decreases, the number of telework companies, wage, and welfare increase. In contrast, bid rent and city boundaries decrease. Former empirical researches showed different evidence for the location of primary telework companies; one is nearby CBD and the other is in the suburban area. However, this is the first paper to demonstrate both results in a model and to show the difference depends on the several key parameters: face-to-face communication cost, commuting cost, and the ratio of labor input for teleworkers in telework companies.

《発表論文》

要旨：Economies transform at an uneven pace: San Jose’s meteoric rise coexists with Detroit’s slow decline. This paper develops a dynamic overlapping generations model of economic geography to explain variation in structural transformation across space and time. In the model, historical exposure to different industries creates persistence in occupational structure, and non-homothetic preferences and differential productivity growth lead to different rates of structural transformation. Despite the heterogeneity across locations, sectors, and time, the model remains tractable and is calibrated to match metropolitan area data for the U.S. economy from 1980 to 2010. The calibration allows us to back out measures of upward mobility and inequality, thereby providing theoretical underpinnings to the Gatsby Curve. The counterfactual analysis shows that structural transformation has substantial effects on mobility: if there were no productivity growth in the service sector, income mobility would be 6 percent higher, and if amenities were equalized across locations, it would rise by 10 percent.

要旨：We propose a scalable regression model with spatially and temporally varying co- efficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and tempo- ral locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation—maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.